Deep learning based suture training system

Mansour, Mohammed; Cumak, Eda Nur; Kutlu, Mustafa; Mahmud, Shekhar

doi:10.1016/j.sopen.2023.07.023

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…We utilize a combined dataset of images from public sources and real-world field captures. This study compares four pre-trained Deep Learning [9] models-InceptionV3, VGG16, VGG19 [10], and ResNet50 [11] to a more basic Convolutional Neural Network (CNN) model. Experiments were performed utilizing diverse combinations of datasets to assess the accuracy of symptom classification.…”

Section: Methodsmentioning

confidence: 99%

“…In entomology and agriculture, VGG16 [18] accurately identifies harmful and beneficial pests. To reduce prediction errors, VGG16 [10] uses insect image datasets to backpropagate neural network weights and biases during training. VGG16 helps scientists and farmers identify, manage, and understand insect ecology in agriculture due to its excellent image recognition performance.…”

Section: Vgg16mentioning

confidence: 99%

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Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Hindarto,

Afarini,

Esthi H

2023

jiko

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This study compares two popular deep-learning models, VGG16 and VGG19, for insect classification. This study aims to evaluate insect detection architectures to automate insect identification. We use a large, heterogeneous dataset of insect species, including common pests and beneficial insects, and their images to achieve this goal. The dataset was used to re-adjust the VGG16 and VGG19 models and analyze their classification performance. With an average improvement of 1,8%, VGG19 outperforms VGG16 in insect classification accuracy. VGG19 is more robust because it can handle complex traits and subtle insect morphology differences. Each architecture's model training duration and computational resources are examined for their practicality in real-world scenarios. This study emphasizes deep learning models in insect classification and shows VGG19's higher accuracy and robustness than VGG16. These findings matter to entomologists, agricultural researchers, and pest control experts. They can improve insect identification accuracy and effectiveness using VGG19-based models, which can help solve insect-related problems in various fields.96.28 percent accuracy was achieved with the implementation of VGG16, according to the experimental findings; 97.07 percent accuracy was obtained with the implementation of VGG19.

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Section: Methodsmentioning

confidence: 99%

Section: Vgg16mentioning

confidence: 99%

Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Hindarto,

Afarini,

Esthi H

2023

jiko

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“…For example, epochs describe how often a model is run through the data during training. This means that each time a dataset is processed by a model, an epoch is completed (Mansour et al, 2023). Since each dataset is different and therefore requires a different optimum, it is advisable to experiment with different hyperparameter settings before performing validation on the test dataset.…”

Section: Model Adaption Validation and Fine-tuningmentioning

confidence: 99%

Automatic identification of role-specific information in product development: a critical review on large language models

Ehring,

Menekse,

Luttmer

et al. 2024

Proc. Des. Soc.

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In the era of digitization and the growing flood of information, the automatic, role-specific identification of information is crucial. This research paper aims to investigate whether the adaptation of LLM is suitable for classifying information obtained from standards for corresponding role profiles. This research reveals that with systematic fine-tuning, prediction accuracy can be increased by almost 100%. The validation was carried out using a two-digit number of standards for three predefined roles and demonstrates the significant potential of LM for labelling content with regard to roles.

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“…Within this particular context, two deep learning architectures [2] that have garnered considerable interest and recognition are VGG16 [3] and MobileNet. The VGG16 [4] model, renowned for its exceptional precision, is a convolutional neural network famous for its capacity to discern intricate characteristics within visual data.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis VGG16 Vs MobileNet Performance for Fish Identification

Hindarto

2023

ijsecs

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This research aims to conduct a comparative evaluation of the efficacy of two neural network architectures in the field of fish identification through the utilization of supervised learning techniques. The evaluation of VGG16 and MobileNet, which are prominent deep learning architectures, has been conducted about their speed, accuracy, and efficiency in resource utilization. To assess the classification performance of both architectures, we employed a dataset encompassing diverse fish categories. The findings indicated that the VGG16 model demonstrated superior accuracy in fish classification, albeit due to increased computational time and resource utilization. On the contrary, MobileNet exhibits enhanced speed and efficiency, albeit at a marginal cost to its accuracy. The findings of this study have the potential to inform the selection of deep learning models for fish recognition scenarios, considering the specific requirements of the task, such as prioritizing accuracy or efficiency. The findings mentioned above offer significant insights that can be utilized in the advancement of Artificial Intelligence (AI)-based applications within the domains of fisheries resource management and environmental monitoring. These applications specifically necessitate precise and effective fish recognition capabilities. The comparison findings indicate that the accuracy achieved by VGG16 was 0.99, whereas MobileNet also attained an accuracy of 0.99.

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Deep learning based suture training system

Cited by 7 publications

References 26 publications

Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Comparison Efficacy of VGG16 and VGG19 Insect Classification Models

Automatic identification of role-specific information in product development: a critical review on large language models

Comparative Analysis VGG16 Vs MobileNet Performance for Fish Identification

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